Description Usage Arguments Value Examples
View source: R/pretty_mixed_models.R
pretty_mixed_effects_coxme() Cox mixed effects model using the coxme() function. Hazard ratios are produced with confidence intervals. P values are also produced. For categorical variables with 3+ levels overall Type 3 p values are calculated, in addition to p values comparing to the first level (reference).
1 | pretty_mixed_effects_coxme(fit, df)
|
fit |
glmer fit |
df |
is data.frame or tibble used to create model fits. Used for capturing variable labels, if they exist |
A data.frame with: Variable
, Level
, HR (95% CI)
, P Value
(for categorical variables comparing to reference), Overall P Value
(for categorical variables with 3+ levels).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | # Mixed effect cox ph model example - random intercepts
library(magrittr)
library(ggeffects)
library(sjmisc)
library(lme4)
library(splines)
library(coxme)
library(car)
library(survival)
library(broom.mixed)
library(rlang)
library(kableExtra)
data(PDACdata)
set.seed(542542522)
#USE THIS ONE for 3 months to 2 years (24 months)
surv_months_obj <- survival::Surv(time = PDACdata$dx_lastcontact_death_months,
event = PDACdata$puf_vital_status == 0)
cactfit <- coxme(surv_months_obj ~
caci3 + surgery_volume_recode + (1|puf_facility_id),
data = PDACdata,
control=coxme.control(eps = 1e-04, toler.chol = .Machine$double.eps^0.75,
iter.max = 200, inner.iter = Quote(max(4, fit0$iter+1)),
sparse.calc = NULL,
optpar = list(method = "BFGS", control=list(reltol = 1e-3)),
refine.df=4, refine.detail=FALSE, refine.method="control",
sparse=c(50, .02),
varinit=c(.02, .1, .4, .8)^2, corinit = c(0, .3)) )
cactfitcmetable <- pretty_mixed_effects_coxme(cactfit, PDACdata)
# for PDF output
# kable(cactfitcmetable , 'latex', escape = FALSE, longtable = T, booktabs = TRUE, linesep = '',
# caption = 'Full Mulitvariable Mixed effects Cox Proportional-Hazards Regression Model with
# interaction for Overall Survival2 to 10 years')
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